Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Analyze DNA/RNA/protein sequences. Use when the user provides a sequence and asks for analysis, translation, GC content, ORFs, motifs, restriction sites, or primer design. Triggers on "sequence", "translate", "GC content", "ORF", "primer", "restriction", "complement", "reverse complement".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Comprehensive sequence analysis using BioPython and command-line tools.
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight
seq = Seq("ATGCGATCGATCGATCG...")
print(f"Length: {len(seq)} bp")
print(f"GC Content: {gc_fraction(seq)*100:.1f}%")
print(f"Complement: {seq.complement()}")
print(f"Reverse Complement: {seq.reverse_complement()}")
print(f"Protein: {seq.translate()}")
from Bio.Seq import Seq
def find_orfs(sequence, min_length=100):
orfs = []
seq = Seq(str(sequence))
for strand, nuc in [("+", seq), ("-", seq.reverse_complement())]:
for frame in range(3):
trans = nuc[frame:].translate()
aa_seq = str(trans)
start = 0
while start < len(aa_seq):
m_pos = aa_seq.find("M", start)
if m_pos == -1:
break
stop_pos = aa_seq.find("*", m_pos)
if stop_pos == -1:
stop_pos = len(aa_seq)
orf_len = (stop_pos - m_pos) * 3
if orf_len >= min_length:
nt_start = frame + m_pos * 3
orfs.append({
"strand": strand,
"frame": frame + 1,
"start": nt_start,
"length_aa": stop_pos - m_pos,
"length_nt": orf_len,
"protein": aa_seq[m_pos:stop_pos]
})
start = stop_pos + 1
return sorted(orfs, key=lambda x: x["length_nt"], reverse=True)
from Bio.Restriction import RestrictionBatch, Analysis
from Bio.Seq import Seq
seq = Seq("ATGCGATCGATCG...")
rb = RestrictionBatch(["EcoRI", "BamHI", "HindIII", "NotI", "XhoI"])
ana = Analysis(rb, seq)
results = ana.full()
for enzyme, sites in results.items():
if sites:
print(f"{enzyme}: cuts at positions {sites}")
from Bio.Seq import Seq
from Bio.SeqUtils import MeltingTemp as mt
def design_primers(seq_str, product_size_range=(200, 800)):
seq = Seq(seq_str)
# Forward primer (first 20bp)
fwd = seq[:20]
fwd_tm = mt.Tm_NN(fwd)
# Reverse primer (last 20bp, reverse complement)
rev = seq[-20:].reverse_complement()
rev_tm = mt.Tm_NN(rev)
print(f"Forward: 5'-{fwd}-3' (Tm={fwd_tm:.1f}°C, GC={gc_fraction(fwd)*100:.0f}%)")
print(f"Reverse: 5'-{rev}-3' (Tm={rev_tm:.1f}°C, GC={gc_fraction(rev)*100:.0f}%)")
print(f"Product size: {len(seq)} bp")
If user provides multiple sequences:
# Write sequences to FASTA file
cat > /tmp/sequences.fa << 'EOF'
>seq1
ATGCGATCG...
>seq2
ATGCAATCG...
EOF
# If clustalw/muscle available, use them
# Otherwise use BioPython's pairwise alignment
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
alignments = pairwise2.align.globalxx(seq1, seq2)
print(format_alignment(*alignments[0]))
*Sequence Analysis Results*
• Length: 1,234 bp
• GC Content: 52.3%
• ORFs found: 3 (longest: 456 aa)
*Protein Translation (frame +1):*
```MRSSIDLK...STOP```
*Restriction Sites:*
• EcoRI: positions 123, 456
• BamHI: position 789
• HindIII: no sites found
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
中文简介正在准备中。当前显示原文说明。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
中文简介正在准备中。当前显示原文说明。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
中文简介正在准备中。当前显示原文说明。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
中文简介正在准备中。当前显示原文说明。